Covid-19 Data Sets

We are collectively in uncharted territory across the globe. The last widespread pandemic was a hundred years ago and the world economy is more interconnected than at any point in history. While our collective first concern is for the health and safety of those affected by the virus, the rest of us are left with the task of keeping the economy running during a time of isolation, business closures and digital connection.

The first question that data scientists are being asked is ‘what does this mean for our business and how long will it last?’ Data scientists are rooted in the numbers and our models often tend to look for the hidden drivers and focus on the long-term trends. Standard leading economic indicators (new housing starts, industrial production indices) are slow to respond due to the cadence of their release but will see drastic swings once the reporting catches up to our current situation. Near term indicators (stock market close, short term interest rates) are seeing an immediate impact but are extremely volatile. This leaves many data scientists with forecasting models that either expect a small down swing based on the coincident indicators but treat the current crisis as a blip. To the other extreme, some models are built to pick up the small variations in the data and are giving wild estimates based on values well outside those seen in the training set.

In order to make the most sense of this ever-changing time, data scientists need to focus on what insights they can provide rather than explaining the reasons why the models aren’t designed to adjust to a level of uncertainty not seen in a generation. There is a threefold path to delivering valuable insights in the current climate: track the virus progression, measure your near-term demand and measure your built-up demand.

Tracking the virus progression is something that dominates the mind of individuals regardless of their background. From the business focused data scientist there are three main measures to inform the economic impact- cases diagnosed, available hospital beds and recovered cases. Cases diagnosed is tied to the availably of testing and the spread of COVID-19. It provides a good proxy for the restrictions set in place on businesses and individuals. Available hospital beds provides an indicator for how well the medical system is dealing with severe cases. The goal in flattening the curve is to keep this number as far above zero as possible. Until this rises to a comfortable level, restrictions are likely to stay in place. Finally, as the number of recovered cases outpaces the number of new cases, this provides an indicator that things are turning a corner for the positive.

Examining your current demand must be focused on actual sales and orders rather than indicators of demand. Scaling a forecast that is slow to react by the y/y drop in sales or orders provides a quick estimate of what may be coming in terms of sales or orders. Traffic to your site may be up but for those locations that rely heavily on a brick and motor presence, in-store sales drop correlated with the three aforementioned COVID-19 indicators provides a good measure of the impact of stay at home restrictions.

Finally, we turn our thoughts to the road out of the crisis. Great minds across the world are focused on treatment and preventions methods to reduce the humanitarian impact. For the business focused data scientist, our role is to help our organizations see when things will rebound and be ready to meet that demand. This is where indicators such as website visits or phone calls can be contrasted with the drop in sales, the expected sales without the crisis (this is where those slow to respond forecasting models still provide value) and the COVID-19 indicators that we have turned a positive corner come together. In tandem the let us know when things are starting to rebound so our organizations can be ready and help lessen the mid-term economic impact by scaling up appropriate once the health threat has receded.